A wide variety of different types of visualizations are utilized in conjunction with data analytics in information processing systems. For example, such visualizations include so-called “tag clouds.” In a typical tag cloud, multiple words or other text strings are simultaneously displayed, each with a size in proportion to its frequency of occurrence in a document, file or other collection of textual data. Conventional tag clouds of this type are commonly used to represent text strings resident in web sites, academic papers, conference proceedings, tweets, blog posts, emails and other textual data sources.
A significant drawback of conventional tag clouds is that they represent the textual data in a seemingly random way within a given space or other display area. Typically, textual data is shown with the size of each text string proportional only to its occurrence frequency. Such an approach is particularly problematic when the number of documents or other textual data sources becomes large, in which case the resulting tag cloud conveys little information and can become very difficult to read, thereby undermining its usefulness as an analytical tool. Unfortunately, this can render conventional tag clouds unsuitable for use in the context of “big data” analytics challenges faced by enterprises in processing complex data sources.